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Bayesian Inference

4,821 papers

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Papers

Domain-Liftability of Relational Marginal Polytopes AISTATS 2020
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning AISTATS 2020
Bandit optimisation of functions in the Matérn kernel RKHS AISTATS 2020
Approximate Inference with Wasserstein Gradient Flows AISTATS 2020
On Thompson Sampling for Smoother-than-Lipschitz Bandits AISTATS 2020
Safe-Bayesian Generalized Linear Regression AISTATS 2020
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization AISTATS 2020
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout AISTATS 2020
Robust Optimisation Monte Carlo AISTATS 2020
Fast Markov chain Monte Carlo algorithms via Lie groups AISTATS 2020
Doubly Sparse Variational Gaussian Processes AISTATS 2020
Black-Box Inference for Non-Linear Latent Force Models AISTATS 2020
Enriched mixtures of generalised Gaussian process experts AISTATS 2020
Bayesian experimental design using regularized determinantal point processes AISTATS 2020
Non-exchangeable feature allocation models with sublinear growth of the feature sizes AISTATS 2020
An approximate KLD based experimental design for models with intractable likelihoods AISTATS 2020
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations AISTATS 2020
Adversarial Robustness Guarantees for Classification with Gaussian Processes AISTATS 2020
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations AISTATS 2020
Amortized Inference of Variational Bounds for Learning Noisy-OR AISTATS 2020
Distributed, partially collapsed MCMC for Bayesian Nonparametrics AISTATS 2020
A Theoretical Case Study of Structured Variational Inference for Community Detection AISTATS 2020
Hamiltonian Monte Carlo Swindles AISTATS 2020
Learning spectrograms with convolutional spectral kernels AISTATS 2020
Stochastic Neural Network with Kronecker Flow AISTATS 2020